Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Olfaction01:25

Olfaction

49.6K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
49.6K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

13.7K
Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
13.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Quantitative sensory testing and classical pain model dataset in 127 healthy volunteers.

Data in brief·2026
Same author

Voronoi tessellation as a complement or replacement for confidence ellipses in the visualization of data projection and clustering results.

PloS one·2026
Same author

Self-organizing neural network-based generative AI with embedded error inflation control enhances effective knowledge extraction from preclinical studies with reduced sample size.

Pharmacological research·2026
Same author

A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data.

Canadian journal of pain = Revue canadienne de la douleur·2026
Same author

Dimensionality-modulated generative AI for safe biomedical dataset augmentation.

iScience·2026
Same author

Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data.

European journal of pain (London, England)·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Mar 13, 2026

Simple and Computer-assisted Olfactory Testing for Mice
06:40

Simple and Computer-assisted Olfactory Testing for Mice

Published on: June 15, 2015

10.9K

Machine-learned pattern identification in olfactory subtest results.

Jörn Lötsch1,2, Thomas Hummel3, Alfred Ultsch4

  • 1Institute of Clinical Pharmacology, Goethe - University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany.

Scientific Reports
|October 21, 2016
PubMed
Summary
This summary is machine-generated.

This study used machine learning to analyze smell test results from over 10,000 individuals. Three distinct smell perception patterns were identified, aiding in understanding olfactory disorders.

More Related Videos

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
09:33

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Published on: March 22, 2018

9.2K
Olfactory Behavioral Testing in the Adult Mouse
09:00

Olfactory Behavioral Testing in the Adult Mouse

Published on: January 28, 2009

20.4K

Related Experiment Videos

Last Updated: Mar 13, 2026

Simple and Computer-assisted Olfactory Testing for Mice
06:40

Simple and Computer-assisted Olfactory Testing for Mice

Published on: June 15, 2015

10.9K
An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
09:33

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Published on: March 22, 2018

9.2K
Olfactory Behavioral Testing in the Adult Mouse
09:00

Olfactory Behavioral Testing in the Adult Mouse

Published on: January 28, 2009

20.4K

Area of Science:

  • Neuroscience
  • Olfactory Research
  • Computational Biology

Background:

  • The human sense of smell comprises olfactory threshold, odor discrimination, and odor identification.
  • Understanding how these components change in different disorders is crucial for olfactory research.

Purpose of the Study:

  • To identify distinct cluster structures within olfactory subtest results using a data-driven approach.
  • To analyze patterns of olfactory function across various olfactory pathologies.

Main Methods:

  • Unsupervised machine learning, specifically Emergent Self-organizing feature maps (ESOM), was applied to olfactory subtest data.
  • The analysis included data from 10,714 subjects with nine different olfactory pathologies.
  • The U-matrix was utilized to identify and characterize distinct clusters.

Main Results:

  • Three distinct clusters emerged: (i) low threshold with good discrimination/identification, (ii) very high threshold with poor/absent discrimination/identification, and (iii) medium threshold with reduced discrimination/identification.
  • Specific olfactory disorder etiologies were unequally distributed across these clusters.
  • Congenital anosmia was overrepresented in cluster (ii), while postinfectious olfactory dysfunction was frequent in cluster (iii).

Conclusions:

  • The study successfully identified a distinct cluster structure in olfactory test patterns.
  • While clusters showed some association with specific etiologies, they did not provide clear separation between them.
  • These findings support continued research in olfactory test pattern recognition for better diagnosis and understanding of smell disorders.